Enterprise AI Analysis
TraceFlow: Efficient Trace Analysis for Large-Scale Parallel Applications via Interaction Pattern-Aware Trace Distribution
TraceFlow offers a 13.49x speedup in trace analysis for large-scale parallel applications by using an interaction pattern-aware trace distribution strategy, significantly reducing inter-process communication.
Key Impact Metrics
Quantifiable improvements TraceFlow brings to complex parallel application analysis.
0
Average Speedup
0
Interaction Analysis Time Reduction
0
Max Processes Supported
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
TraceFlow's core innovation is its interaction pattern-aware trace distribution strategy, which assigns events with interaction relationships to the same replay processes. This approach minimizes inter-process communications during trace analysis, leading to a nearly communication-free analysis.
TraceFlow employs a hybrid method, combining static program structure analysis to extract a Communication Skeleton Tree (CST) and lightweight runtime communication pattern collection. This global perspective guides efficient trace distribution with minimal overhead.
Experimental results demonstrate that TraceFlow achieves an average speedup of 13.49x compared to state-of-the-art tools like Scalasca. It was tested on widely used benchmarks and real-world applications with up to 8,192 processes.
13.49x
Average Speedup over State-of-the-Art
TraceFlow Analysis Workflow
Static Module: CST Extraction
→
Dynamic Module: Pattern Collection
→
Trace Analysis: Interaction-Aware Distribution
→
Preprocessing: Trace Shuffling
→
Parallel Trace Replay
→
Results Gathering
| Feature | Existing Methods | TraceFlow |
|---|---|---|
| Trace Distribution |
|
|
| Communication Overhead |
|
|
| Analysis Speed |
|
|
| Overhead for Distribution |
|
|
Real-World Application: LAMMPS
When analyzing LAMMPS with 16,384 processes, Scalasca collected up to 1.6TB traces. TraceFlow, through its optimized distribution, drastically reduces the analysis time for such large-scale applications. For LAMMPS, TraceFlow achieved a 10.77x speedup over Scalasca.
Calculate Your Potential ROI
See how TraceFlow can significantly improve your operational efficiency and reduce costs.
Estimate Your Savings
Annual Savings
Hours Reclaimed Annually
Implementation Roadmap
Our phased approach ensures a smooth transition and maximized impact.
Phase 1: Static Analysis & CST Generation
Extract program structures and build the Communication Skeleton Tree from executable binaries.
Phase 2: Dynamic Pattern Collection & Embedding
Collect lightweight communication patterns using adaptive sampling and embed them into the CST.
Phase 3: Interaction-Aware Trace Distribution
Distribute trace events to replay processes based on identified interaction patterns to minimize communication.
Phase 4: Parallel Trace Replay & Analysis
Execute trace analysis with minimal inter-RP communication, leveraging local memory access.
Phase 5: Results Gathering & Reporting
Consolidate and report performance metrics, with optimized parallel sorting.
Ready to Transform Your Enterprise?
Connect with our experts to discuss how TraceFlow can revolutionize your parallel application performance.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat